Optimization of application workflow in mobile embedded devices

ABSTRACT

An aspect includes optimizing an application workflow. The optimizing includes characterizing the application workflow by determining at least one baseline metric related to an operational control knob of an embedded system processor. The application workflow performs a real-time computational task encountered by at least one mobile embedded system of a wirelessly connected cluster of systems supported by a server system. The optimizing of the application workflow further includes performing an optimization operation on the at least one baseline metric of the application workflow while satisfying at least one runtime constraint. An annotated workflow that is the result of performing the optimization operation is output.

DOMESTIC PRIORITY

This application is a continuation of U.S. application Ser. No.14/753,685, filed on Jun. 29, 2015, the disclosure of which isincorporated by reference herein in its entirety.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with Government support under Contract No.:HR0011-13-C0022 awarded by Defense Advanced Research Projects Agency(DARPA). The Government has certain rights in this invention.

BACKGROUND

The present disclosure relates generally to optimizing applicationworkflows, and more specifically, to the optimization of applicationworkflows in an environment of mobile embedded devices.

Embedded systems utilize application workflows to operate, and a typicalapplication workflow executed by an embedded system includes diverseapplication segments. Some segments of these workflows are more criticalthan other segments in terms of computational accuracy, speed, andreliability requirements. A key challenge in designing such applicationworkflows is determining optimal or near-optimal voltage-frequencycontrol setting (or other power setting) assignments to a processor ofan embedded system across each application segment, such that theaccuracy, speed, and reliability requirements are met.

For example, an unmanned aerial vehicle (UAV) is an embedded system thatutilizes application workflows to control flight operations andmission-critical engagements. Because the processor of the UAV demandshigh performance (with real-time performance constraints) at low powerand high reliability for flight operations and mission-criticalengagements, designing application workflows includes determiningoptimal or near-optimal assignments of voltage-frequency controlsettings (or other power settings) for the processor of the UAV acrosseach application segment to meet these low power and high reliabilityrequirements.

A contemporary approach is to distribute application workflowcomputations to a cloud-based system. The distribution of applicationworkflow computations attempts to balance the real-time processing needsof the workflow against the low power requirements of the embeddedsystem. Yet, with the distribution of application workflow computations,the embedded systems lose the benefits of localized processing and cansuffer from communication inadequacies between the cloud-based systemand the embedded system. Additionally, the balance can be skewed byenergy costs with respect to communication protocols.

SUMMARY

Embodiments include a method, system, and computer program product foroptimizing an application workflow. The optimizing of the applicationworkflow comprises characterizing the application workflow bydetermining at least one baseline metric related to an operationalcontrol knob of an embedded system processor. Note that the applicationworkflow performs a real-time computational task encountered by at leastone mobile embedded system of a wirelessly connected cluster of systemssupported by a server system. The optimizing of the application workflowfurther comprises performing an optimization operation on the at leastone baseline metric of the application workflow while satisfying atleast one runtime constraint and outputting an annotated workflow. Theannotated workflow is a result of the performing of the optimizationoperation.

Additional features and advantages are realized through the techniquesof the present disclosure. Other embodiments and aspects of thedisclosure are described in detail herein. For a better understanding ofthe disclosure with the advantages and the features, refer to thedescription and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a system for optimizing an application workflow accordingto an embodiment of the present invention;

FIG. 2 depicts a process flow for optimizing an application workflowaccording to an embodiment of the present invention;

FIG. 3 depicts a process flow of a static optimization according to anembodiment of the present invention;

FIG. 4A depicts a process flow of a static optimization according to anembodiment of the present invention;

FIG. 4B depicts a bit mask conversion table according to an embodimentof the present invention;

FIG. 5 depicts a process flow of a directed acyclic graph optimizationaccording to an embodiment of the present invention;

FIG. 6 depicts a process flow of a parallel directed acyclic graphoptimization according to an embodiment of the present invention;

FIG. 7 depicts a process flow of a dynamic optimization according to anembodiment of the present invention;

FIG. 8 depicts a process flow of a dynamic optimization with aturbo-boost feature according to an embodiment of the present invention;

FIG. 9 depicts a process flow of a synthesizer according to anembodiment of the present invention;

FIG. 10 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Embodiments described herein relate to optimizing application workflows,and more specifically, to the optimization of application workflows inan environment of mobile embedded devices. This optimization is providedvia a software framework for static preparation of application workflowsand for simulated dynamic deployment of statically optimized applicationworkflows, prior to actual deployment in a mobile embedded deviceenvironment.

The mobile embedded device environment, in general, includes a pluralityof embedded devices with processors that wirelessly interconnect with acloud-based system to perform cooperative computations with real-timeconstraints. For example, a plurality of embedded devices, such asunmanned aerial vehicles (UAVs), can wirelessly communicate to scan animage of the ground with cameras. Cooperative computations can beperformed with a cloud-based system on the image to infer an actualground status and recognize objects in real-time. Prior to the actualoperation of scanning the ground, embodiments of the present inventionmaximize within real-time constraints (e.g., within a given time, aresilience/reliability metric, a maximum power, etc.) a performance andpower efficiency (e.g., performance per watt; Giga-operation per watt,Giga-floating operation per watt, user defined operations, etc.) of theprocessors of the UAVs by static preparation and simulated dynamicdeployment of the UAV workflows.

FIG. 1 depicts a modeling system 100 for optimizing an applicationworkflow according to an embodiment of the present invention. Themodeling system 100 resides in a computing device via any combination ofsoftware and hardware to perform the operations described herein. Themodeling system 100 includes, via any combination of software andhardware, an interface 110 that includes a runtime optimizer 114 as wellas a static optimizer 112 that includes a set of compiler flags 113. Themodeling system 100 also includes via any combination of software andhardware, a performance model 116, a power model 117, a resilience model118, and voltage sensitivity models 119. The modeling system 100 is alsoin communication with a workflow synthesizer 140 and an environmentalmodel and stress tests module 150 to assist in the optimization of theapplications of interest 130.

The interface 110 can be a resilience aware application programminginterface. The resilience aware application programming interface can bea smart graphic user interface for entering commands and configurationsby the user and for outputting visualizations of the modeling system100. In this way, the interface 110 provides the medium through which auser can interact with the modeling system 100. The user can interactwith two main entities: the static optimizer 112 and the runtimeoptimizer 114.

The static optimizer 112 identifies and determines baseline metrics forthe application workflow and then modifies these baseline metrics togenerate the annotated workflow. The static optimizer 112 includeswidgets. The widgets can be set by the static optimizer 112 to changepower performance characteristics of a processor of the target embeddedsystem. For instance, the widgets are variable settings or knobs (suchas, adaptive power performance control knobs) that control/scale aperformance of a processor running an application workflow. Theperformance of the modeled (target) processor running the applicationcan be determined by power, efficiency, and power over performanceefficiency metrics, such as performance per watt; Giga-operation perwatt, Giga-floating operation per watt, user defined operations, etc.For example, a performance can be scaled when a value of a widget thatcorresponds to voltage, frequency, a size of the structure, etc. ischanged. In this way, an application workflow can be optimized bychanging the value of a widget from a baseline to a desired value. Thestatic optimizer 112 also includes the compiler flags 113 that alsochange performance, as well as power over performance efficiency,depending on a level of optimization that is used by a compiler of themodeling system 100.

The performance model 116, the power model 117, and the resilience model118 separately capture how performance, power, and resilience (of thetarget embedded processor system) change over time when running anapplication workflow. The voltage sensitivity models 119 specify inanalytical form the dependency between performance/power/resilience andvoltage. For example, as a voltage setting of the static optimizer 112is changed (i.e., as the voltage knob is adjusted), the changes inperformance/power/resilience are captured by the voltage sensitivitymodels 119. These models can be software models that mimic a targetprocessor for determining how an application workflow performs underdifferent settings, or can be measurement-based models corresponding tonative hardware that directly runs the application workflow.

The runtime optimizer 114 executes a dynamic deployment simulation ofthe statically prepared workflow. The dynamic deployment simulationmimics actual deployment conditions that test the performance and powerefficiency of the processor executing the statically prepared workflow.During the dynamic simulation, on-the-fly optimization of the adaptiveperformance knobs is performed by the modeling system 100. For example,the runtime optimizer 114 emulates a deployment environment of themobile embedded device environment and optimizes baseline metrics of theapplication workflow operating in this emulated deployment environment.Further, the runtime optimizer 114 can inject faults and per-applicationexecution time deviations into the emulated deployment environment.

In another example, the runtime optimizer 114 statistically simulatesruntime effects of a deployment environment by injecting uncertaintiesinto the simulation. Uncertainties include unknown variations, events,or occurrences with the deployment environment (e.g., encounter anobstacle, adverse condition, adverse object, etc.) that affect theembedded system during actual deployment. Then, in spite of theoptimized settings of the statically prepared workflow, a firstapplication segment does not meet its deadline due to the injection ofan unknown occurrence. In turn, the runtime optimizer 114 can furtherinvoke an optimization on-the-fly to determine new settings for voltageand frequency so that the application workflow meets the deadline. Notethat the on-the-fly optimization can cause the real-time constraints tobe surpassed.

The applications of interest 130 are inputs of the modeling system 100.The annotated applications 132 are outputs of the modeling system 100(e.g., the result of any application workflow being optimized oroptimized workflow, a.k.a. annotated workflow). Note that the annotatedapplication 132 can be re-tuned by the modeling system 100 to factor insimulated run-time uncertainties caused by the operational environmentof said embedded system processor. Further, the optimization operationand/or the retuning operation can comprise a guard mechanism to minimizea number of failures during the optimization operation (e.g., eachfailure being inability by the respective operation to find anoptimization solution within a given set of constraints).

An application workflow can be a single application, a linear workflowof a plurality of applications, a directed acyclic graph (DAG) workflowof a plurality of applications, a complex workflow, etc. The applicationworkflow can also include segments, each of which can correspond to anapplication of the workflow and/or a portion of an application of theworkflow. In this way, the modeling system 100 serves as an emulatorthat optimizes segments of an application workflow to output anoptimized workflow or annotated workflow, which is ensured to workwithin the real-time constraints of an actual deployment environment.

Further, the workflow synthesizer 140 can be utilized to synthesize theapplications of interest 130 into an application workflow for input intothe modeling system 100. The environmental model and stress tests module150 can be utilized to provide information and assumptions about thedeployment environment to test and simulate the application workflow.

The optimization by the modeling system 100 will now be described withrespect to FIG. 2, which depicts a process flow 200 according to anembodiment of the present invention. The process flow 200 begins atblock 210, where the modeling system 100 receives (as the applicationworkflow) the applications of interest 130 that once deployed will beexecuted by an embedded system within a deployment environment. Themodeling system 100 will also receive the real-time constraints of thedeployment environment that the application workflow must meet. In anexample embodiment, a user may instruct the modeling system 100 todownload the applications of interest 130 and/or utilize the workflowsynthesizer 140 to synthesize arbitrary workflows from the applicationsof interest 130.

Next, at block 220, the static optimizer 112 of the modeling system 100executes a static preparation of the application workflow. In theexample embodiment, a user may invoke the static optimizer 112 on theapplications of interest 130 and/or synthesized arbitrary workflows.When invoked, the static optimizer 112 characterizes the applications ofinterest 130 and/or synthesized arbitrary workflows in terms ofperformance, power, and resilience via a model (e.g., models 116-119) orby direct measurement of native hardware. Characterization comprisesexecuting any given application to identify and determine performance,power, and resilience metrics of that application as a function of time.Native performance, power, and resilience variations over time areidentified and determined by the static optimizer 112 to producebaseline characteristics or metrics. The static optimizer 112 canoptimize these baseline metrics by defining processor settings. Notethat the static optimizer 112 can make certain assumptions of what thedeployment environment will be to optimize these variations.

For instance, the modeling system 100 defines processor settings withrespect to each segment of the application workflow. To define processorsettings, the modeling system 100 utilizes adaptive power-performancecontrol knobs to change a performance and power efficiency of aprocessor with respect to the applications of interest 130 and/orsynthesized arbitrary workflows. An example of an adaptive knob is adynamic voltage and frequency scaling (DVFS) knob that adjusts (andsets) per application or segment the voltage and frequency of aprocessor of an embedded system towards more efficiency or higherperformance. Other examples of knobs include, but are not limited to,dynamic voltage scaling (DVS) knobs, dynamic frequency scaling (DFS)knobs, power per-core power-gating (PCPG) knobs, and compileroptimization level knobs. The modeling system 100 maximizes thesesettings via the knobs within the real-time constraints of thedeployment environment. Once the settings are identified and determined,a statically prepared workflow is produced for dynamic testing.

Next, at block 230, the runtime optimizer 114 of the modeling system 100executes a dynamic deployment simulation of the statically preparedworkflow. The dynamic deployment simulation mimics actual deploymentconditions that test the performance and power efficiency of theprocessor executing the statically prepared workflow. During the dynamicsimulation, on-the-fly optimization of the adaptive performance knobs isperformed by the modeling system 100.

For instance, while the settings are statically optimized and set forthe processor in block 220, the dynamic deployment simulation ispermitted to deviate from the static settings to account for simulatedunknown occurrences. By simulating various ideal conditions along withunknown occurrences, the modeling system 100 generates an optimizedworkflow. Thus, at block 240, the modeling system 100 deploys theoptimized workflow (e.g., an annotated application 132).

Note that the process flow 200 includes a feedback loop, which providesas input information discovered during dynamic deployment simulation tosubsequent static preparations (so that in the next iteration ofexecuting a static preparation, the information can be used to build amore accurate workflow). For example, the annotated application 132 canbe re-tuned by the modeling system 100 to factor in simulated run-timeuncertainties caused by the operational environment of said embeddedsystem processor. Further, the optimization operation and/or theretuning operation can comprise a guard mechanism to minimize a numberof failures during the optimization operation.

In view of the above, a baseline optimization of a single application(or application segment) will now be described with respect to FIG. 3.FIG. 3 depicts a process flow 300 of a static optimization according toan embodiment of the present invention. Particularly, the process flow300 illustrates an example of scaling a frequency to maximize theperformance per watt of a processor of an embedded system when executingthe single application. The process flow 300 can be referred to as aSingleOpt heuristic (e.g., single optimization heuristic).

The process flow 300 beings at block 310, where the static optimizer 112sets a DVFS knob to a highest frequency point for the singleapplication. Then, at decision block 310, the static optimizer 112determines if a runtime of the single application will meet apredetermined deadline with the frequency set to the highest point onthe DVFS knob. If the single application does meet the deadline, thenthe process flow 300 proceeds to block 315 as indicated by the ‘YES’arrow, where the static optimizer 112 sets the DVFS knob to a next lowerfrequency. Then, then the process flow 300 proceeds back to decisionblock 310, where the static optimizer 112 determines if a runtime of thesingle application will meet a predetermined deadline with the frequencyset to the next point on the DVFS knob. This is repeated until afrequency is found that does not meet the predetermined deadline. Whenthe frequency that does not meet the predetermined deadline is found,the process proceeds to block 320 as indicated by the ‘NO’ arrow. Atblock 320, the static optimizer 112 returns the DVFS knob to theprevious frequency (i.e., the frequency that met the deadline).

At decision block 350, the static optimizer 112 determines if the singleapplication will meet a resilience constraint. If the single applicationdoes not meet the resilience constraint, then the process flow 300proceeds to block 330 as indicated by the ‘NO’ arrow, where the staticoptimizer 112 sets the DVFS knob to a next higher frequency. Then, theprocess flow 300 proceeds back to decision block 3325, where the staticoptimizer 112 determines if the resilience constraint is met. This isrepeated until a frequency is found that does meet the resilienceconstraint. When the frequency that does meet the resilience constraintis found, the DVFS knob is set to the identified frequency and theprocess proceeds to decision block 340 as indicated by the ‘YES’ arrow.

At decision block 349, the static optimizer 112 determines if the singleapplication will meet a power constraint at the identified frequency. Ifthe single application does not meet the power constraint, then theprocess flow 300 proceeds to block 350 as indicated by the ‘NO’ arrow,where the static optimizer 112 identifies that there is no solutiongiven the predetermined deadline, resilience constraint, and powerconstraint. If the single application does meet the power constraint,then the process flow 300 proceeds to block 355 as indicated by the‘YES’ arrow, where the static optimizer 112 identifies that there is asolution given the predetermined deadline, resilience constraint, andpower constraint.

Another embodiment of a static optimization performed by the staticoptimizer 112 will now be described with respect to FIG. 4. FIG. 4depicts a process flow 400 of a linear static optimization according toan embodiment of the present invention. Particularly, the process flow400 illustrates an example of scaling frequencies of multipleapplications to maximize the performance per watt of a processor of anembedded system. The process flow 400 can be referred to as a LinOptheuristic (e.g., linear optimization heuristic).

The process flow 400 beings at block 410, where the static optimizer 112receives a linear workflow including three applications. A linearworkflow, in general, is a set of diverse application strung togethersequentially. Next, at block 420, the static optimizer 112 converts afrequency of each of the applications in the linear workflow in to a bitmask M. In general, a bit mask can be multiple bits in a byte, nibble,word, etc., that can be set either on, off, or inverted from on to off(or vice versa) to designate a frequency of an application. FIG. 4B,Table 499 (e.g., a Bit Mask Conversion Table) shows an example bit maskconversion of the linear workflow.

At block 430, the static optimizer 112 calculates runtime, power, andresilience constraints utilizing the bit masks. To calculate the runtimeconstraint, the static optimize 110 determines whether the sum of eachruntime T multiplied by the bit mask M, at the particular i-thfrequency, is less than the sum of the deadline as shown in equation 1.To calculate the power constraint, the static optimize 110 determines apower P for the i-th application in linear workflow according toequation 2. To calculate the resilience constraint, the static optimize110 determines a resilience R based on the sum of each application'sresilience R_(i) multiplied by the bit mask M_(i) according to equation3.

With the runtime, power, and resilience constraints calculated, theprocess flow 400 proceeds to block 440, where the static optimizer 112utilizes a solver to determine a minimized energy according to equation4, where E is the energy for the i-th application in linear workflow. Inone example, the solver calculates the lowest frequency per applicationof the linear workflow. At block 450, the solver of the static optimizer112 outputs a solution with respect to the determination of the lowestfrequency.

$\begin{matrix}{{\Sigma\;{T_{i} \cdot M_{i}}} \leq {Deadline}} & {{Equation}\mspace{14mu} 1} \\{{P_{i} \cdot M_{i}} \leq P_{\max}} & {{Equation}\mspace{14mu} 2} \\{{{\Sigma\;{R_{i} \cdot M_{i}}} \leq R} = {\Sigma\frac{\Sigma\;{FIT}_{i} \times T_{i}}{\Sigma\; T_{i}}}} & {{Equation}\mspace{14mu} 3} \\{\Sigma\;{E_{i} \cdot M_{i}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

Another embodiment of a static optimization performed by the staticoptimizer 112 will now be described with respect to FIG. 5. FIG. 5depicts a process flow 500 of a DAG optimization by the static optimizer112 that can include repeated invocations of the LinOpt heuristicaccording to an embodiment of the present invention. The process flow500 can be referred to as a DAGOpt or DAGOpt-S heuristic (e.g., directacyclic graph optimization or structured direct acyclic graphoptimization).

The process flow 500 begins at block 502, where the static optimizer 112initializes all applications within a DAG workflow. A DAG workflow is adirected graph with no directed cycles, formed by a collection ofvertices/nodes and directed edges, where each edge connects one node toanother. A node can correspond a segment of a workflow (e.g., anapplication of the workflow and/or a portion of an application of theworkflow). Thus, in block 502, the static optimizer 112 initializes eachnode of the DAG workflow to the lowest frequency level so that each nodeis characterized by the execution time at that level.

Then, at block 504, the static optimizer 112 finds a first critical pathof a first application of the DAG workflow. The first critical path is asequence nodes connected by the edges (e.g., a sequence in which asubset of applications is performed within the DAG workflow). Then, atblock 506, the static optimizer 112 invokes the LinOpt heuristic on thefirst critical path to optimally characterize each node of that firstcritical path.

At decision block 508, the static optimizer 112 determines whether everyapplication of the workflow has been optimized. For example, the staticoptimizer 112 checks as to whether the LinOpt heuristic has been invokedon every node of the DAG workflow. If every node has been optimized,then the process flow 500 proceeds to block 509 as seen by the ‘YES’arrow, where the static optimization of the DAG workflow ends. If everynode has not been optimized, then the process flow 500 proceeds to block510 as seen by the ‘NO’ arrow, where the static optimizer 112 finds thenext critical path (e.g., second or subsequent critical path).

Next, at block 512, the static optimizer 112 removes any nodes from thesecond critical path that have been optimized and subtracts thecorresponding runtimes from a total runtime. Then, at block 514, thestatic optimizer 112 invokes the LinOpt heuristic on the second criticalpath to optimally characterize the remaining nodes of that secondcritical path.

At decision block 516, the static optimizer 112 determines whether theLinOpt heuristic of block 512 failed. If the LinOpt heuristic has notfailed, then the process flow 500 returns to block 508 as seen by the‘NO’ arrow, where the static optimizer 112 again determines whetherevery application of the workflow has been optimized.

If the LinOpt heuristic has failed, then the process flow 500 proceedsto block 518 as seen by the ‘YES’ arrow, where the static optimizer 112performs a restoration operation. The restoration returns the nodescorresponding to the failed LinOpt to their pre-failure state. Then, atblock 520, the static optimizer 112 invokes the LinOpt heuristic on thealready optimally nodes to adjust their characterization so that nofuture LinOpt heuristic failures occur. Next, the process flow 500returns to block 508, where the static optimizer 112 again determineswhether every application of the workflow has been optimized.

Another embodiment of a static optimization performed by the staticoptimizer 112 will now be described with respect to FIG. 6. FIG. 6depicts a process flow 600 of a parallel directed acyclic graphoptimization by the static optimizer 112 according to an embodiment ofthe present invention. The process flow 600 can also be referred to as aDAGOpt or DAGOpt-P heuristic (e.g., direct acyclic graph optimization orparallel direct acyclic graph optimization).

The process flow 500 begins at block 602, where the static optimizer 112initializes all applications with a DAG workflow. Then, at block 604,the static optimizer 112 finds a longest path for each application ofthe DAG workflow to create a set of longest paths. At block 606, thestatic optimizer 112 removes all duplicate paths from a set of longestpaths. With all duplicates removed, the static optimize, as shown inblock 608, invokes the LinOpt heuristic on the remaining paths tooptimize each path. Next, at block 610, the static optimizer 112 selectsthe highest setting for each application.

In view of the above, an embodiment of a dynamic optimization performedby the runtime optimizer 114 will now be described with respect to FIG.7. In general, the runtime optimizer 114 utilizes a deadline targetagainst which the application workflow is measured. The dynamicoptimization models time variations during execution of the applicationworkflow as conditions change. The aim of modeling the time variationsis to identify whether a real execution time of the application workflowis longer than the estimated deadline. In view of this aim, FIG. 7depicts a process flow 700 of a dynamic optimization of an applicationworkflow by the runtime optimizer 114 according to an embodiment of thepresent invention.

The process flow 700 begins at block 702, where the runtime optimizer114 initializes variables for the dynamic optimization. Then, in block704, the runtime optimizer 114 invokes the LinOpt heuristic with adeadline adjusted by a conservative factor. The conservative factoraccounts for delays in execution. At block 705, the runtime optimizer114, for jth segment, initiates with the optimization mandated DVFSsettings while keeping track of an actual execution time. At block 706,the runtime optimizer 114 subtracts the elapsed time from the deadline.In the dynamic optimization scheme, the initial LinOpt invocationsupplies statically mandated voltage-frequency setting, but the actual“elapsed time” is obtained by some sort of dynamic simulation of theactual deployed environment. For example, the “actual” elapsed time issimulated by adding a random adder (plus or minus) to the staticallycalculated time. The random adder can be obtained from a user-specifiedprobability distribution function.

At decision block 708, the runtime optimizer 114 determines whether thelast segment has been processed. If the last segment has not beenprocessed, then the process flow 700 proceeds to block 710 as seen bythe ‘NO’ arrow, where the runtime optimizer 114 compares elapsed timewith statistically anticipated time. Then, at decision block 712, theruntime optimizer 114 determines whether there is a mismatch between thecalculated execution time from blocks 704-706 and a staticallyanticipated time, threshold, or deadline. If there is a mismatch, thenthe process flow 700 proceeds to block 704 as seen by the ‘YES’ arrow,where the runtime optimizer 114 again invokes the LinOpt heuristic witha deadline adjusted by a conservative factor. If there is no mismatch,then the process flow 700 proceeds to block 705 as seen by the ‘NO’arrow, where the runtime optimizer 114 again invokes the LinOptheuristic with a deadline adjusted by a conservative factor.

Returning to decision block 708, if the last segment has been processed,then the process flow 700 proceeds to block 714 as seen by the ‘YES’arrow, where the runtime optimizer 114 determines if the applicationworkflow meets the deadline. If the deadline is not met, then theprocess flow 700 proceeds to block 716 as seen by the ‘NO’ arrow, wherethe runtime optimizer 114 identifies the dynamic optimization as failed.If the deadline is met, then the process flow 700 proceeds to block 718as seen by the ‘YES’ arrow, where the runtime optimizer 114 identifiesthe dynamic optimization as a success.

Another embodiment of a dynamic optimization performed by the runtimeoptimizer 114 will now be described with respect to FIG. 8. FIG. 8depicts a process flow 800 of a dynamic optimization by the runtimeoptimizer 114 with a turbo-boost feature according to an embodiment ofthe present invention.

The process flow 800 begins at block 802, where the runtime optimizer114 initializes variables for the dynamic optimization. Then, in block804, the runtime optimizer 114 invokes the LinOpt heuristic with adeadline adjusted by a conservative factor. The conservative factoraccounts for delays in execution. At block 806, the runtime optimizer114 estimates a probability of deadline failure.

At decision block 808, the runtime optimizer 114 determines whether theprobability of deadline failure is grater that a threshold. If theprobability of deadline failure is not grater that a threshold, then theprocess flow 800 proceeds to blocks 810 and 812 as seen by the ‘NO’arrow, where the runtime optimizer 114 initiates optimization andsubtracts the elapsed time from the deadline.

If the probability of deadline failure is greater that a threshold, thenthe process flow 800 proceeds to block 814 as seen by the ‘YES’ arrow,where the runtime optimizer 114 performs a turbo-boost operation beforeproceeding to block 812. The turbo-boost operation or “mode” comprisesboosting of voltage-frequency levels during short intervals. Thisvoltage-frequency level boost enables the application workflow to meetof real-time deadline controls, while violating hard fail rate (ormaximum power) constraints for those short intervals. Yet, theturbo-boost operation does not affect long-term endurance or reliabilitytargets of the clustered mobile embedded systems (or any individualembedded system thereof).

After the elapsed time is subtracted, the runtime optimizer 114determines whether the last segment has been processed at decision block816. If the last segment has not been processed, then the process flow800 proceeds to block 818 as seen by the ‘NO’ arrow, where the runtimeoptimizer 114 compares elapsed time with statistically anticipated time.Then, at decision block 820, the runtime optimizer 114 determineswhether there is a mismatch between the calculated execution time fromblocks 810 and 812 and a statically anticipated time, threshold, ordeadline. If there is a mismatch, then the process flow 800 proceeds toblock 804 as seen by the ‘YES’ arrow, where the runtime optimizer 114again invokes the LinOpt heuristic with a deadline adjusted by aconservative factor. If there is no mismatch, then the process flow 800proceeds to block 806 as seen by the ‘YES’ arrow, where the runtimeoptimizer 114 estimates a probability of deadline failure.

Returning to decision block 816, if the last segment has been processed,then the process flow 800 proceeds to block 822 as seen by the ‘YES’arrow, where the runtime optimizer 114 determines if the applicationworkflow meets the deadline. If the deadline is not met, then theprocess flow 800 proceeds to block 824 as seen by the ‘NO’ arrow, wherethe runtime optimizer 114 identifies the dynamic optimization as failed.If the deadline is met, then the process flow 800 proceeds to block 826as seen by the ‘YES’ arrow, where the runtime optimizer 114 identifiesthe dynamic optimization as a success.

In view of the above, an embodiment of a synthetization performed by theworkflow synthesizer 140 will now be described with respect to FIG. 9.FIG. 9 depicts a process flow 900 of a synthesizing by the workflowsynthesizer 140 according to an embodiment of the present invention. Theprocess flow 900 begins at block 905 where application segments arereceived by the workflow synthesizer 140. Once received, the workflowsynthesizer 140 performs a randomized linear synthesizing (block 906)and a randomized DAG synthesizing (block 908). Then, at block 910, theworkflow synthesizer 140 performs a fast analytical estimator ofworkflow quality or invokes simulation/emulation. Next, at block, theworkflow synthesizer 140 performs go/no-go decision based on workflowquality assessment. At this time, it the decision was a no-go, theprocess flow may proceed to block 905. Otherwise, the applicationsegments that are synthesized are now ready for use by the model system100.

Embodiments of the present invention may be a system (e.g., implementedon a cloud computing environment), a method, and/or a computer programproduct, or a model thereof.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10, a schematic of an example of a cloud computingnode is shown. Cloud computing node 1010 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 1010 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 1010 there is a computer system/server 1012,which is operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with computer system/server 1012 include, butare not limited to, personal computer systems, server computer systems,thin clients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 1012 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 1012 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 10, computer system/server 1012 in cloud computing node1010 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 1012 may include, but are notlimited to, one or more processors or processing units 1016, a systemmemory 1028, and a bus 1018 that couples various system componentsincluding system memory 1028 to processor 1016.

Bus 1018 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 1012 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 1012, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 1028 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 1030 and/orcache memory 1032. Computer system/server 1012 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 1034 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 1018 by one or more datamedia interfaces. As will be further depicted and described below,memory 1028 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 1040, having a set (at least one) of program modules1042, may be stored in memory 1028 by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystem, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules 1042 generally carry outthe functions and/or methodologies of embodiments of the invention asdescribed herein.

Computer system/server 1012 may also communicate with one or moreexternal devices 1014 such as a keyboard, a pointing device, a display1024, etc.; one or more devices that enable a user to interact withcomputer system/server 1012; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 1012 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 1022. Still yet, computer system/server1012 can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter 1020. As depicted,network adapter 1020 communicates with the other components of computersystem/server 1012 via bus 1018. It should be understood that althoughnot shown, other hardware and/or software components could be used inconjunction with computer system/server 1012. Examples, include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 11, illustrative cloud computing environment 1150is depicted. As shown, cloud computing environment 1150 comprises one ormore cloud computing nodes 1010 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1154A, desktop computer 1154B, laptopcomputer 1154C, and/or automobile computer system 1154N may communicate.Nodes 1010 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1150to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1154A-N shown in FIG. 11 are intended to be illustrative only and thatcomputing nodes 1010 and cloud computing environment 1150 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment 1150 (FIG. 11) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 12 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 1260 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1261;RISC (Reduced Instruction Set Computer) architecture based servers 1262;servers 1263; blade servers 1264; storage devices 1265; and networks andnetworking components 1266. In some embodiments, software componentsinclude network application server software 1267 and database software1268.

Virtualization layer 1270 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1271; virtual storage 1272; virtual networks 1273, including virtualprivate networks; virtual applications and operating systems 1274; andvirtual clients 1275.

In one example, management layer 1280 may provide the functionsdescribed below. Resource provisioning 1281 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1282provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1283 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1284provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1285 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1290 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1291; software development and lifecycle management 1292;virtual classroom education delivery 1293; data analytics processing1294; transaction processing 1295; and application workload optimization1296.

In view of the above, an embodiment of the present invention can includea method, where the method includes emulating, characterizing andoptimizing an application workflow that represents the real-timecomputation task encountered by a wirelessly connected cluster of mobileembedded systems, supported by a ground server system.

In another embodiment or according to the method embodiment above, anobjective function in the optimization can be any user-specifiedcomposite metric consisting of power (energy), performance, andresilience components.

In another embodiment or according to any of the method embodimentsabove, the application workflow can include a plurality of individualapplications that can be targeted for per-application optimizationcontrols.

In another embodiment or according to any of the method embodimentsabove, the optimization can be done with any user-specified soft and/orhard failure rate limit constraints.

In another embodiment or according to any of the method embodimentsabove, a performance-related constraint can be a real-time executiondeadline.

In another embodiment or according to any of the method embodimentsabove, an objective function can be any specified energy efficiencymetric (e.g., Giga-operations per second, where “operations” can be userdefined).

In another embodiment or according to any of the method embodimentsabove, applications of the application workflow can be organized as adirected acyclic graph.

In another embodiment or according to any of the method embodimentsabove, applications of the application workflow can be a lineardependence chain across the selected applications.

In another embodiment or according to any of the method embodimentsabove, the optimization can be at the time of static analysis, prior toactual run-time emulation.

In another embodiment or according to any of the method embodimentsabove, the optimization can be during the run-time emulation of theapplication workflow.

In another embodiment or according to any of the method embodimentsabove, a run-time emulation/optimization can encounter injected faultsand per-application execution time deviations (e.g., uncertainties).

In another embodiment or according to any of the method embodimentsabove, the method can utilize on a per-application basis optimizationcontrols. The optimization control can be one or more power-performanceknobs, including but not limited to DVS, DFS, DVFS, PCPG, or otheradaptive power-performance management control knobs.

In another embodiment or according to any of the method embodimentsabove, the method can include a “turbo” mode boosting ofvoltage-frequency levels for short intervals to enable meeting ofreal-time deadline controls, while violating hard fail rate (or maximumpower) constraints for those short intervals, without affecting longterm endurance or reliability targets of any mobile embedded system.

In another embodiment, a system or a computer program product isprovided. The system or the computer program product can implement anyof the method embodiments above.

Technical effects and benefits of embodiments of the present inventioninclude controlling (or simulating the control of) a system of(wirelessly connected), cloud-backed mobile embedded systems through theoptimization of application workflows, to ensure successful operationswithin stipulated criteria at a maximum power-performance efficiency(e.g., maximum Gigaflops per watt). Further, the technical effects andbenefits of embodiments of the present invention include the ability tomanipulate DVFS/other control settings and compiler optimization levelsduring the static “preparation” of a complex workflow, while addressingdynamic uncertainty in the field.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiments were chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for optimizing an application workflow,comprising: characterizing, by a processor coupled to a memory, theapplication workflow by determining at least one baseline metric relatedto an operational control knob of an embedded system processor, theapplication workflow configured to perform a real-time computationaltask encountered by at least one mobile embedded system of a wirelesslyconnected cluster of systems supported by a server system; performing,by the processor, an optimization operation on the at least one baselinemetric of the application workflow while satisfying at least one runtimeconstraint; and outputting, by the processor, an annotated workflow, theannotated workflow being a result of the performing of the optimizationoperation.
 2. The method of claim 1, wherein the optimization operationcomprises a static preparation of the application workflow, the staticpreparation comprising: characterizing the at least one baseline metricof the application workflow as a function of time; and optimizing the atleast one baseline metric within the at least one runtime constraint. 3.The method of claim 2, wherein the static preparation executes a singleoptimization heuristic.
 4. The method of claim 2, wherein the staticpreparation executes a linear optimization heuristic.
 5. The method ofclaim 2, wherein the static preparation executes a structured directacyclic graph optimization.
 6. The method of claim 1, wherein theoptimization operation comprises a dynamic optimization of theapplication workflow, the dynamic optimization comprising: emulating adeployment environment of the mobile embedded system(s); and optimizingthe at least one baseline metric of the application workflow operatingin the deployment environment within the at least one runtimeconstraint.
 7. The method of claim 6, wherein the dynamic optimizationexecutes a turbo-boost optimization, the turbo boost optimizationcomprising a boosting of voltage-frequency levels used for shortintervals to meet a real-time deadline of the emulated deploymentenvironment.
 8. The method of claim 6, wherein the dynamic optimizationcomprises: injecting per-application execution time deviations into theemulated deployment environment.
 9. The method of claim 1, wherein theat least one runtime constraint is a performance-related constraint of areal-time execution deadline.
 10. The method of claim 1, wherein theoptimization operation comprises adjusting a processor setting of one ofthe mobile embedded systems by changing a value of an adaptive powerperformance control knob, the processor setting corresponding to abaseline performance, a baseline power, or a baseline resilience of theone of the mobile embedded systems.
 11. The method of claim 1, furthercomprising: re-tuning the annotated workflow to factor in simulatedrun-time uncertainties caused by the operational environment of saidembedded system processor.
 12. The method of claim 1, wherein theoptimization operation comprises a guard mechanism to minimize a numberof failures during the optimization operation, wherein each failurerepresents an inability by the optimization operation to find anoptimization solution within the at least one runtime constraint. 13.The method of claim 1, comprising: synthesizing, by the processor, aplurality of individual applications targeted for per-applicationoptimization controls into a synthesized workflow based on the at leastone runtime constraint, a plurality of metrics, hardware resourceconstraints of the mobile embedded systems, and software redundancyoptions; and receiving, by the processor, a synthesized workflow as theapplication workflow.